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AuthorNashed, Mohamad Shadi
AuthorMohamed, M Shadi
AuthorShady, Omar Tawfik
AuthorRenno, Jamil
Available date2024-06-02T06:20:08Z
Publication Date2022
Publication NameFatigue and Fracture of Engineering Materials and Structures
ResourceScopus
Identifierhttp://dx.doi.org/10.1111/ffe.13660
ISSN8756758X
URIhttp://hdl.handle.net/10576/55704
AbstractMany experiments are usually needed to quantify probabilistic fatigue behavior in metals. Previous attempts used separate artificial neural network (ANN) to calculate different probabilistic ranges which can be computationally demanding for building probabilistic fatigue constant life diagram (CLD). Alternatively, we propose using probabilistic neural network (PNNs) which can capture data distribution parameters. The resulted model is generative and can quantify aleatoric uncertainty using a single network. Two tests are presented. The first captures the fatigue life aleatoric uncertainty for P355NL1 steel and successfully builds a probabilistic fatigue CLD. The resulted network is not only more efficient but also provides higher accuracy compared with ANN. To assess fatigue, the second test examines vibrations of a pipework assembly. The proposed methodology quantifies the nonlinear relation between the vibration velocity and the equivalent stress and successfully reflects measurements uncertainties in fatigue assessment. The proposed methodology is published in opensource format (https://github.com/MShadiNashed/probabilistic-machine-learning-for-fatigue-data).
SponsorThis project was graciously sponsored by the Qatar National Research Fund (a member of Qatar Foundation) via the National Priorities Research Project under grant NPRP-11S-1220-170112.
Languageen
PublisherJohn Wiley and Sons Inc
Subjectartificial neural network (ANN)
failure probability
fatigue
fatigue life prediction
probabilistic method
vibration
TitleUsing probabilistic neural networks for modeling metal fatigue and random vibration in process pipework
TypeArticle
Pagination1227-1242
Issue Number4
Volume Number45
dc.accessType Abstract Only


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